Indeed, one of the most important activities we are daily involved in is interacting with other human beings. For this reason, we developed the ability to forecast human motion and adapt our behavior accordingly [1]}, [2]}, [3]}.
However, in order to encode asynchronous quick reactions to human activities, a basic but fundamental task to solve is the estimation of human pose from event-based streams.
Human pose estimation is already widely adopted in action recognition [4]}, [5]}, human tracking [6]}, sport assistance [7]}, and virtual reality [8]}. Most of the adopted solutions involve using multiple cameras and require the subjects to wear special markers suites [8]}. Despite their efficiency and broad adoption, these techniques rely on delicate synchronization and are difficult to deploy in real environments. For these reasons, monocular human pose estimation represents a fascinating research challenge with growing interests in the industry [10]}, [11]}, [12]}. There are two different families of solutions to solve 3D human pose estimation: skeleton-based and model-based solutions. The former regresses skeletal 3D joints from a planar image [13]}, [14]}, [15]}, while the latter fits a tri-dimensional parametric model of the human body to the subjects in the scene [16]}, [17]}. Recently, Xu adapted a model-based approach to event-cameras [18]}. Although they underline an interesting solution, their approach requires RGB images to guide the tracking and cannot be used for real-time applications, as it relies on a time-consuming optimization phase.
